AI Benchmark

BLEU Score

A metric for evaluating the quality of machine-generated text by comparing it to reference translations or texts.

Perplexity

A measurement of how well a probability model predicts a sample, commonly used to evaluate language model performance.

F1 Score

A harmonic mean of precision and recall, providing a single metric that balances both false positives and false negatives.

Confusion Matrix

A table used to evaluate classification model performance by comparing predicted classifications against actual classifications.

ROC Curve

A graphical plot illustrating the diagnostic ability of a binary classifier as its discrimination threshold is varied.

AUC Score

Area Under the ROC Curve, a single metric summarising a classifier's ability to distinguish between classes.

Precision

The ratio of true positive predictions to all positive predictions, measuring accuracy of positive classifications.

Recall

The ratio of true positive predictions to all actual positive instances, measuring completeness of positive identification.

TinyML

Machine learning techniques optimised to run on microcontrollers and extremely resource-constrained embedded devices.

Quantisation

Reducing the precision of neural network weights and activations from floating-point to lower-bit representations for efficiency.

An attention mechanism that selectively computes relationships between a subset of input tokens rather than all pairs, reducing quadratic complexity in transformer models.

The simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction.

A hypothetical form of AI that possesses the ability to understand, learn, and apply knowledge across any intellectual task a human can perform.

A sequence of data processing and model execution steps that automate the flow from raw data to AI-driven outputs.

A structured representation of real-world entities and the relationships between them, used by AI for reasoning and inference.

The process of removing redundant or less important parameters from a neural network to reduce its size and computational cost.

Artificial intelligence algorithms processed locally on edge devices rather than in centralised cloud data centres.

A documentation framework that provides standardised information about an AI model's intended use, performance characteristics, limitations, and ethical considerations.